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Paddle/python/paddle/v2/framework/tests/test_reduce_op.py

90 lines
2.5 KiB

import unittest
import numpy as np
from op_test import OpTest
class TestSumOp(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestMeanOp(OpTest):
def setUp(self):
self.op_type = "reduce_mean"
self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float32")}
self.attrs = {'dim': 1}
self.outputs = {'Out': self.inputs['X'].mean(axis=self.attrs['dim'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestMaxOp(OpTest):
"""Remove Max with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
self.op_type = "reduce_max"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
self.attrs = {'dim': -1}
self.outputs = {'Out': self.inputs['X'].max(axis=self.attrs['dim'])}
def test_check_output(self):
self.check_output()
class TestMinOp(OpTest):
"""Remove Min with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
self.op_type = "reduce_min"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
self.attrs = {'dim': 2}
self.outputs = {'Out': self.inputs['X'].min(axis=self.attrs['dim'])}
def test_check_output(self):
self.check_output()
class TestKeepDimReduce(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
self.attrs = {'dim': -2, 'keep_dim': True}
self.outputs = {
'Out': self.inputs['X'].sum(axis=self.attrs['dim'], keepdims=True)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class Test1DReduce(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random(20).astype("float32")}
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()